Systems and methods for split rendering of Network Based Media Processing (NBMP) are provided. A method includes deriving a network based media processing (NBMP) workflow, and assigning a plurality of workflow tasks of the NBMP workflow among a media sink, a media source, and at least one cloud element, network element, or edge element. A first subset of the plurality of the workflow tasks are assigned to the media source, a second subset of the plurality of the workflow tasks are assigned to the media sink, and a third subset of the plurality of the workflow tasks are assigned to the at least one cloud element, network element, or edge element. The first subset, the second subset, and the third subset do not overlap with each other.
Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method performed by at least one processor, the method comprising: deriving a network based media processing (NBMP) workflow; and assigning a plurality of workflow tasks of the NBMP workflow among a media sink, a media source, and at least one cloud element, network element, or edge element, wherein a first subset of the plurality of the workflow tasks are assigned to the media source, wherein a second subset of the plurality of the workflow tasks are assigned to the media sink, wherein a third subset of the plurality of the workflow tasks are assigned to the at least one cloud element, network element, or edge element, and wherein the first subset, the second subset, and the third subset do not overlap with each other.
This invention relates to distributed media processing in network-based media processing (NBMP) systems, addressing the challenge of efficiently allocating media workflow tasks across heterogeneous computing resources. The method involves deriving an NBMP workflow, which defines a sequence of operations for processing media content, such as encoding, transcoding, or analysis. The workflow tasks are then distributed among three distinct categories of computing elements: a media source (e.g., a camera or storage device), a media sink (e.g., a display or storage destination), and at least one intermediate processing element, which may be a cloud server, network element (e.g., a router or switch), or edge device (e.g., a local gateway or edge server). The tasks are partitioned into three non-overlapping subsets: one assigned to the media source, another to the media sink, and the remaining to the intermediate processing elements. This distribution optimizes resource utilization by leveraging the capabilities of each element, such as offloading computationally intensive tasks to cloud or edge resources while minimizing latency by processing certain tasks locally at the source or sink. The method ensures efficient media processing by avoiding redundant task assignments and balancing workloads across the network.
2. The method of claim 1 , further comprising: receiving capabilities information from at least one from among the media source and the media sink, wherein the capabilities information describes resources of the at least one from among the media source and the media sink that are available, wherein the assigning comprises assigning the workflow tasks based on the capabilities information.
Media processing systems often require efficient distribution of tasks between media sources and sinks to optimize performance. A method addresses this by dynamically assigning workflow tasks based on the available resources of the media source and sink. The method involves receiving capabilities information from at least one of the media source or the media sink, where this information describes the resources (such as processing power, memory, or bandwidth) that are available. The workflow tasks are then assigned based on this capabilities information to ensure optimal utilization of resources. This approach improves efficiency by matching tasks to the most suitable components, reducing bottlenecks and enhancing overall system performance. The method can be applied in various media processing applications, including video encoding, transcoding, or streaming, where resource allocation directly impacts quality and speed. By dynamically adjusting task distribution, the system adapts to changing conditions, ensuring consistent performance even as resource availability fluctuates.
3. The method of claim 2 , further comprising: reconfiguring the NBMP workflow based on a change of at least one of the resources of the at least one from among the media source and the media sink.
This invention relates to dynamic workflow management in media processing systems, specifically addressing the challenge of adapting workflows in response to changes in available resources. The system involves a media processing workflow that transfers media data between a media source and a media sink, where the media source may include devices like cameras or storage systems, and the media sink may include storage, displays, or further processing units. The workflow is initially configured based on the capabilities and constraints of these resources, such as bandwidth, processing power, or storage capacity. The key innovation is the ability to automatically reconfigure the workflow when there is a change in any of the resources involved. For example, if the bandwidth between the media source and sink decreases, the system adjusts the workflow to optimize data transfer, such as by reducing resolution or frame rate. Similarly, if a new media sink becomes available, the workflow may be updated to include additional processing steps or output destinations. The reconfiguration ensures continuous and efficient media processing despite dynamic resource conditions. This approach enhances flexibility and reliability in media workflows, particularly in environments where resource availability fluctuates, such as cloud-based or distributed systems.
4. The method of claim 3 , further comprising: receiving an event-driven notification from the media source or the media sink in a case where the at least one of the resources associated with the at least one from among the media source and the media sink is reduced below a predetermined threshold, wherein the reconfiguring the NBMP workflow accommodates the at least one of the resources that is reduced.
This invention relates to dynamic resource management in media processing workflows, specifically addressing the challenge of maintaining workflow continuity when resource availability is reduced. The system monitors resources associated with media sources or sinks, such as bandwidth, processing power, or memory, and detects when these resources fall below predefined thresholds. Upon detecting such a reduction, the system receives an event-driven notification from the affected media source or sink. In response, the system reconfigures a Network-Based Media Processing (NBMP) workflow to adapt to the reduced resources, ensuring uninterrupted media processing. The workflow reconfiguration may involve adjusting parameters like bitrate, resolution, or processing steps to accommodate the constrained resources while maintaining operational integrity. This approach enables real-time adaptation to resource fluctuations, preventing disruptions in media streaming or processing tasks. The system integrates with existing media processing frameworks, dynamically adjusting workflows without manual intervention. The invention is particularly useful in environments where resource availability is variable, such as cloud-based media services or edge computing scenarios. By proactively responding to resource changes, the system enhances reliability and efficiency in media workflows.
5. The method of claim 1 , wherein the at least one cloud element, network element, or edge element is a plurality of network elements or a plurality of cloud nodes, the assigning comprises dividing the third subset of the plurality of the workflow tasks of the NBMP workflow between the plurality of network elements or the plurality of cloud nodes based on capabilities of the plurality of network elements or of the plurality of cloud nodes, and the method further comprises dynamically reconfiguring the NBMP workflow based on a change in availability of the plurality of network elements or of the plurality of cloud nodes.
This invention relates to distributed workflow management in network and cloud environments, addressing the challenge of efficiently allocating and dynamically adjusting workflow tasks across multiple network or cloud elements. The method involves dividing a subset of workflow tasks from a network-based management protocol (NBMP) workflow among multiple network elements or cloud nodes based on their capabilities. The distribution ensures optimal utilization of resources by matching task requirements with the capabilities of each element. Additionally, the system monitors the availability of these elements and dynamically reconfigures the workflow in response to changes, such as resource unavailability or performance fluctuations. This adaptability enhances reliability and efficiency in executing complex workflows across distributed environments. The solution is particularly useful in scenarios where tasks must be processed by diverse elements with varying capabilities, ensuring seamless operation even as resources become available or unavailable. The dynamic reconfiguration aspect allows the system to maintain performance and continuity without manual intervention.
6. The method of claim 1 , further comprising: receiving capabilities information from the at least one cloud element, network element, or edge element, wherein the capabilities information describes available resources of the at least one cloud element, network element, or edge element, wherein the assigning comprises assigning the workflow tasks based on the capabilities information.
This invention relates to a system for dynamically assigning workflow tasks across distributed computing elements, such as cloud, network, or edge devices, to optimize resource utilization and task execution. The problem addressed is the inefficient allocation of tasks in distributed environments, where resources may be underutilized or overloaded due to a lack of real-time awareness of available capabilities. The method involves receiving capabilities information from one or more cloud, network, or edge elements, where this information describes the available resources of each element, such as processing power, memory, storage, or network bandwidth. Based on this capabilities information, workflow tasks are assigned to the most suitable elements to ensure efficient execution. The system dynamically adjusts task distribution in response to changes in resource availability, ensuring optimal performance and load balancing across the distributed infrastructure. This approach improves resource utilization, reduces latency, and enhances overall system efficiency by leveraging real-time insights into the capabilities of each computing element.
7. The method of claim 6 , wherein the at least one cloud element, network element, or edge element is at least one cloud node.
This invention relates to distributed computing systems, specifically methods for managing and optimizing resource allocation in cloud, network, and edge computing environments. The problem addressed is the inefficient utilization of computing resources across distributed nodes, leading to performance bottlenecks, increased latency, and higher operational costs. The method involves dynamically allocating and reallocating tasks or workloads among multiple cloud, network, or edge elements based on real-time performance metrics such as processing capacity, network latency, and energy consumption. A central controller or distributed intelligence monitors these elements to determine optimal task distribution, ensuring balanced load and minimizing resource waste. The system may also prioritize tasks based on their criticality or urgency, further optimizing performance. In one embodiment, the method specifically focuses on cloud nodes, which are computing resources deployed in a cloud infrastructure. These nodes may include virtual machines, containers, or physical servers. The system dynamically adjusts task assignments to these nodes to maintain high availability and scalability while reducing costs. The method may also incorporate predictive analytics to anticipate future workload demands and preemptively allocate resources, enhancing overall system efficiency. This approach is particularly useful in environments where workloads are highly variable, such as in cloud-based applications, IoT deployments, or edge computing scenarios.
8. The method of claim 6 , wherein the at least one cloud element, network element, or edge element is a plurality of cloud elements, network elements, or edge elements, and the capabilities information includes at least one parameter that describes a connection characteristics between two of the plurality of cloud elements, network elements, or edge elements.
This invention relates to a system for managing and optimizing network resources in distributed computing environments, such as cloud, network, and edge computing systems. The problem addressed is the lack of efficient mechanisms to assess and utilize the capabilities of multiple interconnected elements in these environments, which can lead to suboptimal resource allocation and performance. The invention provides a method for collecting and analyzing capabilities information from a plurality of interconnected elements, such as cloud, network, or edge elements. These elements may include servers, storage devices, networking hardware, or other computing resources distributed across a network. The capabilities information includes parameters that describe the connection characteristics between any two of these elements, such as bandwidth, latency, reliability, or other performance metrics. By gathering and analyzing this information, the system can make informed decisions about resource allocation, load balancing, and task scheduling to improve overall system efficiency. The method involves identifying the plurality of elements and their interconnections, then collecting and storing the relevant capabilities information. This data is used to determine the optimal configuration and utilization of the elements to meet specific performance requirements. The system can dynamically adjust resource allocation based on real-time changes in the capabilities information, ensuring adaptive and efficient operation of the distributed computing environment. This approach enhances scalability, reliability, and performance in complex networked systems.
9. The method of claim 8 , wherein the plurality of cloud elements, network elements, or edge elements is a plurality of cloud nodes.
This invention relates to distributed computing systems, specifically methods for managing and optimizing the deployment of computational tasks across a network of cloud nodes. The problem addressed is the efficient allocation and execution of tasks in a distributed environment to improve performance, reduce latency, and optimize resource utilization. The method involves dynamically selecting and configuring a plurality of cloud nodes to execute computational tasks. These cloud nodes may include cloud elements, network elements, or edge elements, but in this specific embodiment, they are explicitly defined as cloud nodes. The selection process considers factors such as node availability, computational capacity, network latency, and task requirements to determine the optimal nodes for task execution. The method ensures that tasks are distributed in a way that minimizes resource contention and maximizes efficiency. Additionally, the method may include monitoring the performance of the selected cloud nodes during task execution and dynamically adjusting the allocation of tasks based on real-time data. This adaptive approach allows the system to respond to changing conditions, such as fluctuations in network traffic or node availability, ensuring consistent performance. The overall goal is to provide a scalable and resilient framework for distributed computing that can handle varying workloads while maintaining high efficiency.
10. The method of claim 8 , wherein the at least one parameter is a set of parameters that indicate a maximum bandwidth and a minimum latency in two directions between the two of the plurality of cloud elements, network elements, or edge elements.
This invention relates to optimizing network performance in distributed systems, such as cloud, network, or edge computing environments. The problem addressed is ensuring efficient communication between multiple elements (cloud, network, or edge) by dynamically adjusting parameters to balance bandwidth and latency requirements. The method involves defining a set of parameters that specify a maximum bandwidth and a minimum latency for bidirectional communication between any two elements in the system. These parameters are used to configure and manage the connections, ensuring that data transfer meets performance criteria while avoiding bottlenecks. The solution dynamically adapts to changing network conditions, prioritizing critical traffic paths and optimizing resource allocation. By enforcing these constraints, the system ensures reliable and predictable performance for applications requiring low-latency, high-bandwidth communication. This is particularly useful in scenarios where real-time processing, high-speed data transfer, or strict service-level agreements (SLAs) are necessary. The approach can be applied to various network architectures, including hybrid cloud, multi-cloud, or edge computing deployments, to enhance overall system efficiency and responsiveness.
11. A workflow manager of a media system, the workflow manager comprising: at least one processor; and memory comprising computer code, the computer code comprising: workflow deriving code configured to cause the at least one processor to derive a network based media processing (NBMP) workflow; and task assigning code configured to cause the at least one processor to assign a plurality of workflow tasks of the NBMP workflow among a media sink, a media source, and at least one cloud element, network element, or edge element, wherein a first subset of the plurality of the workflow tasks are assigned to the media source, wherein a second subset of the plurality of the workflow tasks are assigned to the media sink, wherein a third subset of the plurality of the workflow tasks are assigned to the at least one cloud element, network element, or edge element, and wherein the first subset, the second subset, and the third subset do not overlap with each other.
This invention relates to a workflow manager for a media system, specifically addressing the challenge of efficiently distributing media processing tasks across different system components. The workflow manager includes at least one processor and memory containing computer code. The code includes workflow deriving code that generates a network-based media processing (NBMP) workflow, which defines a sequence of tasks required to process media content. Additionally, task assigning code distributes these tasks among a media source, a media sink, and at least one cloud, network, or edge element. The tasks are divided into three non-overlapping subsets: one assigned to the media source, another to the media sink, and the remaining to the cloud, network, or edge elements. This distribution optimizes resource utilization by leveraging the capabilities of each component, ensuring efficient media processing while minimizing redundancy. The system dynamically assigns tasks based on their requirements, such as computational power, latency, or bandwidth, to enhance overall performance. This approach is particularly useful in media systems where tasks like encoding, transcoding, or content delivery need to be managed across distributed environments.
12. The workflow manager of claim 11 , wherein the task assigning code is configured to cause to the at least one processor to assign the workflow tasks based on capabilities information that is received from at least one from among the media source and the media sink, and the capabilities information describes resources of the at least one from among the media source and the media sink that are available.
This invention relates to a workflow manager system for media processing, addressing the challenge of efficiently distributing tasks in a media processing pipeline where different components have varying capabilities. The system dynamically assigns workflow tasks based on real-time capabilities information received from media sources and sinks, ensuring optimal resource utilization. Media sources and sinks provide details about their available resources, such as processing power, bandwidth, or storage capacity. The workflow manager uses this information to allocate tasks to the most suitable components, improving efficiency and performance. The system may also handle task dependencies, ensuring that tasks are executed in the correct order while considering the capabilities of each component. This approach enhances scalability and adaptability in media processing workflows, allowing the system to handle varying workloads and resource constraints effectively. The invention is particularly useful in environments where media processing tasks must be distributed across heterogeneous devices or systems with different capabilities.
13. The workflow manager of claim 12 , wherein the computer code further comprises: workflow reconfiguring code configured to cause the at least one processor to reconfigure the NBMP workflow based on a change of at least one of the resources of the at least one from among the media source and the media sink.
This invention relates to a workflow manager system for managing media processing workflows, particularly in environments where media sources and sinks (destinations) may change dynamically. The system addresses the challenge of maintaining efficient media processing when resources such as media sources or sinks are modified, ensuring seamless adaptation without manual intervention. The workflow manager includes computer code that monitors and manages a media processing workflow, such as a Network-Based Media Processing (NBMP) workflow. The system is designed to detect changes in resources, such as the addition, removal, or modification of media sources or sinks. When such changes occur, the workflow reconfiguring code automatically adjusts the NBMP workflow to accommodate the new resource configuration. This reconfiguration ensures that media processing continues uninterrupted, optimizing resource utilization and maintaining performance. The system dynamically adapts to changes in media sources or sinks, such as when a new media source is added or an existing sink is removed. The reconfiguration process may involve rerouting media streams, adjusting processing parameters, or redistributing tasks among available resources. This automated approach reduces the need for manual intervention, improving efficiency and reliability in media processing environments. The invention is particularly useful in applications requiring real-time media processing, such as broadcasting, streaming, or multimedia production.
14. The workflow manager of claim 13 , wherein the workflow reconfiguring code is configured to cause the at least one processor to reconfigure the NBMP workflow to accommodate reduction of the at least of the resources based on receiving an event-driven notification from the media source or the media sink in a case where the at least one of the resources associated with the at least one from among the media source and the media sink is reduced below a predetermined threshold.
This invention relates to a workflow manager for media processing systems, specifically addressing the challenge of dynamically adjusting workflows in response to resource constraints. The system monitors resources associated with media sources or sinks (e.g., input/output devices, processing units) and automatically reconfigures workflows when resource availability falls below a predefined threshold. The workflow manager includes code that triggers reconfiguration upon receiving an event-driven notification from the media source or sink, ensuring continuous operation without manual intervention. The reconfiguration may involve redistributing tasks, adjusting processing parameters, or rerouting data to maintain performance despite reduced resources. This adaptive approach prevents system failures or performance degradation when hardware or software resources become limited, such as during peak loads or hardware degradation. The solution is particularly useful in real-time media processing environments where uninterrupted operation is critical, such as live broadcasting or streaming applications. The system ensures efficient resource utilization while maintaining service continuity.
15. The workflow manager of claim 14 , wherein the at least one cloud element, network element, or edge element is a plurality of network elements or a plurality of cloud nodes, the task assigning code is configured to cause the at least one processor to divide the third subset of the plurality of the workflow tasks of the NBMP workflow between the plurality of network elements or the plurality of cloud nodes based on capabilities of the plurality of network elements or of the plurality of cloud nodes, and the workflow reconfiguring code is configured to cause the at least one processor to dynamically reconfigure the NBMP workflow based on a change in availability of the plurality of network elements or of the plurality of cloud nodes.
This invention relates to a workflow manager for network-based mobile packet (NBMP) workflows, addressing the challenge of efficiently distributing and managing tasks across distributed computing resources. The system dynamically assigns and reconfigures workflow tasks among multiple network elements or cloud nodes based on their capabilities and availability. The workflow manager includes task assigning logic that divides a subset of workflow tasks among a plurality of network elements or cloud nodes, optimizing task distribution according to each node's processing, storage, or network capabilities. Additionally, workflow reconfiguring logic dynamically adjusts the NBMP workflow in response to changes in resource availability, such as node failures, capacity fluctuations, or network conditions. This ensures continuous and efficient execution of the workflow despite varying resource states. The system enhances scalability and reliability in distributed computing environments by intelligently balancing workloads and adapting to dynamic infrastructure changes.
16. The workflow manager of claim 15 , wherein the task assigning code is configured to cause the at least one processor to assign the workflow tasks based on capabilities information that is received from the at least one cloud element, network element, or edge element, and the capabilities information describes available resources of the at least one cloud element, network element, or edge element.
This invention relates to a workflow management system for optimizing task distribution in cloud, network, and edge computing environments. The system addresses the challenge of efficiently allocating workflow tasks across heterogeneous computing resources by dynamically assessing and leveraging their capabilities. The workflow manager includes a task assigning module that distributes workflow tasks based on real-time capabilities information received from cloud, network, or edge elements. This capabilities information details the available resources of each element, such as processing power, memory, storage, and network bandwidth. By analyzing this data, the system ensures tasks are assigned to the most suitable elements, improving performance and resource utilization. The workflow manager also includes a monitoring module that tracks the execution of tasks and a reporting module that generates insights into task performance and resource usage. These components enable continuous optimization of task distribution and proactive identification of bottlenecks. The system supports dynamic adjustments in response to changes in resource availability or workload demands, ensuring efficient task execution across distributed computing environments. This approach enhances scalability, reduces latency, and maximizes resource efficiency in cloud, network, and edge computing scenarios.
17. The workflow manager of claim 16 , wherein the at least one cloud element, network element, or edge element is at least one cloud node.
A system for managing workflows in distributed computing environments addresses the challenge of efficiently coordinating tasks across heterogeneous computing resources, including cloud, network, and edge elements. The system includes a workflow manager that dynamically allocates and monitors tasks across these elements to optimize performance, resource utilization, and reliability. The workflow manager is designed to handle diverse computing environments, ensuring seamless execution of workflows regardless of the underlying infrastructure. The workflow manager interfaces with at least one cloud node, which may be a virtualized or physical computing resource within a cloud infrastructure. The cloud node executes tasks assigned by the workflow manager, leveraging cloud-based scalability and flexibility. The system ensures that tasks are distributed based on availability, capacity, and performance metrics of the cloud nodes, enabling efficient workload balancing. Additionally, the workflow manager may incorporate fault tolerance mechanisms to handle failures in cloud nodes, ensuring continuous operation of the workflow. The system is particularly useful in scenarios requiring dynamic resource allocation, such as real-time data processing, distributed computing, and edge computing applications. By integrating cloud nodes into the workflow management framework, the system enhances adaptability and scalability, allowing for efficient execution of complex workflows across distributed environments.
18. The workflow manager of claim 16 , wherein the at least one cloud element, network element, or edge element is a plurality of cloud elements, network elements, or edge elements, and the capabilities information includes at least one parameter that describes a connection characteristics between two of the plurality of cloud elements, network elements, or edge elements.
This invention relates to a workflow manager system for managing and optimizing the deployment and operation of distributed computing resources across cloud, network, and edge elements. The system addresses the challenge of efficiently coordinating diverse computing resources in heterogeneous environments, where connectivity, performance, and resource availability vary dynamically. The workflow manager collects and maintains capabilities information for each element, including processing power, storage capacity, and network connectivity. This information is used to dynamically assign tasks to the most suitable elements based on real-time conditions. The system also tracks inter-element connection characteristics, such as latency, bandwidth, and reliability, to optimize data flow and task distribution across multiple elements. The workflow manager dynamically adjusts task assignments and data routing based on changing conditions, such as network congestion or element availability. It ensures efficient resource utilization while maintaining performance and reliability. The system supports scalability by managing a plurality of elements, allowing seamless integration of additional resources as needed. By leveraging detailed connection metrics, the system minimizes bottlenecks and maximizes throughput in distributed computing environments.
19. The workflow manager of claim 18 , wherein the at least one parameter is a set of parameters that indicate a maximum bandwidth and a minimum latency in two directions between the two of the plurality of cloud elements, network elements, or edge elements.
This invention relates to a workflow manager for optimizing communication between cloud, network, and edge elements in a distributed computing environment. The problem addressed is ensuring efficient and reliable data transfer by managing bandwidth and latency constraints between interconnected elements. The workflow manager monitors and controls communication parameters, including maximum bandwidth and minimum latency, in both directions between any two elements in the system. These parameters define the performance thresholds required for data exchange, ensuring that data flows meet specified quality-of-service (QoS) requirements. The manager dynamically adjusts these parameters based on real-time conditions, such as network congestion or resource availability, to maintain optimal performance. The system includes multiple cloud, network, or edge elements interconnected in a distributed architecture. The workflow manager evaluates the communication links between these elements, applying the defined bandwidth and latency constraints to prioritize data flows and allocate resources accordingly. This ensures that critical data transfers are not delayed or degraded due to network limitations. By enforcing these constraints, the invention improves reliability and efficiency in distributed computing environments, particularly where low-latency and high-bandwidth communication is essential. The solution is applicable in scenarios such as real-time data processing, cloud computing, and edge computing applications where performance consistency is critical.
20. A non-transitory computer-readable medium storing computer code that is configured to, when executed by at least one processor that implements a workflow manager of a media system, cause the at least one processor to: derive a network based media processing (NBMP) workflow; and assign a plurality of workflow tasks of the NBMP workflow among a media sink, a media source, and at least one cloud element, network element, or edge element, wherein a first subset of the plurality of the workflow tasks are assigned to the media source, wherein a second subset of the plurality of the workflow tasks are assigned to the media sink, wherein a third subset of the plurality of the workflow tasks are assigned to the at least one cloud element, network element, or edge element, and wherein the first subset, the second subset, and the third subset do not overlap with each other.
This invention relates to distributed media processing in a network-based media system. The system addresses the challenge of efficiently distributing media processing tasks across multiple components, including media sources, media sinks, and cloud, network, or edge elements, to optimize performance and resource utilization. The system derives a network-based media processing (NBMP) workflow, which defines a sequence of tasks required to process media content. These tasks are then assigned to different components in the system. A first subset of tasks is assigned to the media source, which may include preprocessing steps such as encoding or transcoding. A second subset of tasks is assigned to the media sink, which may include post-processing steps like decoding or rendering. A third subset of tasks is assigned to cloud, network, or edge elements, which may handle intermediate processing steps like filtering, analysis, or transcoding. The task assignments are non-overlapping, ensuring that each task is processed by only one component, thereby avoiding redundancy and improving efficiency. By distributing tasks in this manner, the system optimizes resource usage, reduces latency, and enhances scalability. The approach is particularly useful in media systems where processing demands vary dynamically, such as in live streaming, video conferencing, or cloud-based media services. The system ensures that tasks are executed in the most appropriate location, whether at the source, sink, or intermediate processing elements, to achieve optimal performance.
Cooperative Patent Classification codes for this invention. Click any code to explore related patents in that topic.
March 18, 2021
April 5, 2022
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.